... the website of the Learning, Artificial Intelligence, and Robotics Laboratory (LAIRLab) at Carnegie Mellon led by Drew Bagnell

Submodular Contextual Policy for List Prediction

The SCP (Submodular Contextual Policy) algorithm is designed for domains that involve predicting a list of options. Typical applications include news recommendation, robotic trajectory selection and document summarization, etc. These applications typically require a nice balance between diversity and quality. SCP achieves this via leveraging online submodular optimization: with a single no-regret online learner, we can compete with an optimal sequence of predictions.

The algorithm ensures both computational and data efficiency. We have released our code on Github. We will be glad to know if it helped in your research!